Conventional enterprise network systems may currently employ multiple solutions for understanding customer interactions by pulling data from a plurality of data sources. Traditionally, structured data stored in relational databases has been the source for conducting analytics to understand such customer interactions, however unstructured data (e.g., plain text) is taking on a greater role to complement analytics and generate actionable insights.
Correspondingly, there is a need to generate data that helps better understand why customers contact enterprise service centers, and generate data indicating what actions or resolutions are occurring during each interaction. The data providing such insights may be generated by analyzing the semantics and latent themes found within the unstructured text in transcription files of inbound calls and/or online-chat sessions, between customers and member service representatives (MSRs), and other contact vectors (e.g., survey file submissions). Each day, a system may interact with customers through any number of channels, yielding unstructured text from the transcriptions of hundreds of thousands of telephone calls and thousands of online chat sessions, as well as text from thousands of emails, and the text from any number of other contact files, such as surveys and Twitter®.
Previously, teams of human analysts read thousands of call transcripts, online chat transcripts, and other files, to distill key emerging themes. Yet the amount of data that must be ingested by humans is not only a costly activity, but it is also an impossible task for humans to identify all of the hidden themes that could provide actionable insights. Speech and text (e.g., chat, emails, surveys) are often the primary forms of communication with customers, and provide the means for an enterprise to enhance experiences and relationships with customers. However, traditional approaches to analyzing speech and text typically require a human analyst to either listen to calls or read transcripts of calls or online chat session. Additionally, some current proprietary text analytics technologies may require a user to have some prior knowledge of the contents of a corpus, and have limited interactive features, both of which limit the amount of insights that a human analyst can glean from the corpus of customer interactions. What is needed is an intuitive framework for all levels of users to quickly gain actionable insights that is not dependent upon or subject to human deficiencies and inefficiencies. Moreover, what is also needed is a means for a machine to ingest data from any number of disparate data sources, having any number of formats or no formatting, and identify the context for each contact to determine the themes, reasons, and solutions associated with each customer contact event.